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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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An Efficient Framework Classification for Diabetic Retinopathy Using MobileNetV2

An Efficient Framework Classification for Diabetic Retinopat...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： Jayaprakash Venugopal Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Diabetes is a disorder marked by unregulated glucose levels in the body, potentially causing retinal damage and leading to irreversible visual impairment or loss. Diabetes affects the eyes, resulting in diabetic retinopathy, a prevalent medical disorder, especially among the elderly. Consequently, timely and precise diabetic retinopathy screening is essential for disease prediction. Nonetheless, conventional manual detection is labor-intensive and often results in misdiagnosis for several individuals. In this study, we devised a deep learning (DL) model using transfer learning from the MobileNetV2 architecture, accompanied with an innovative color version preprocessing approach. It decreased the training duration and achieved an average accuracy of 0.93 when applied to the new Kaggle dataset “APTOS 2019 Blindness Detection.” Furthermore, to mitigate the issue of overfitting in the long term, we used Stratified K-fold cross-validation.

关键词： Training Deep learning Diabetic retinopathy Accuracy Visual impairment Transfer learning computer architecture Color Blindness Overfitting

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms

A Mango Disease Prediction for Smart Agiculture using Machin...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Lavanya K A. Packialatha Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops" A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored.

关键词： Support vector machines Deep learning Plant diseases Accuracy Costs Biological system modeling Crops Data models Agriculture Internet of Things

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Design and Analysis of various Computational Control Algorithms for a Multivariable Process 3

Design and Analysis of various Computational Control Algorit...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Zrigan, Ahmed F. Janani, R. Olwane, Mahmood M. Shanab, Walid T. Libyan Academy for Postgraduate Studies Department of Electrical & Computer Engineering Tripoli Libya Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Electronics and Instrumentation Engineering Tamilnadu Kanchipuram India Higher Institute of Engineering Technologies Department of Electronics Technology Tripoli Libya

ISBN: (纸本)9798350319897

This Paper focuses on the application of Particle Swarm Optimization (PSO) in analyzing, controlling, and comparing two models of multivariable PID controllers in the context of distillation columns. The PSO algorithm, frequently used in PID implementation, is based on heuristic approach and experience to deliver superior performance in the industry. However, for stability and interaction analysis, the Relative Gain Array and Niederlinski index are utilized respectively, for the multivariable "system first order plus time delay"systems. To prevent unfavorable interactions, a decoupled system and full matrix controller are created for one of these systems. The ultimate goal is to achieve the optimum outcome possible through the use of PSO and other analysis methods. © 2023 IEEE.

关键词： Distillation columns

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FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning

FedSOL: Stabilized Orthogonal Learning with Proximal Restric...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Gihun Lee Minchan Jeong Sangmook Kim Jaehoon Oh Se-Young Yun Graduate School of AI KAIST Department of Electrical and Computer Engineering UBC Samsung Advanced Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge, it can also hinder local learning by interfering with local objectives. To address this problem, we propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which adopts an orthogonal learning strategy to balance the two conflicting objectives. FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically, FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios.

关键词： Degradation Data privacy computer vision Federated learning Perturbation methods Aggregates Data models

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Soft Artificial Synapse Electronics

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Research 2024年第4期8卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

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Diabetic Retinopathy Identification From Retinal Images With The Use Of A Deep Learning-Based Transfer Training Framework

Diabetic Retinopathy Identification From Retinal Images With...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Jayaprakash Venugopal Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Diabetic retinopathy (DR) is a major contributor to vision loss worldwide, highlighting the urgent need for early identification and prompt care to prevent further deterioration of vision. Diagnosing DR is intrinsically difficult, since it requires the thorough evaluation of complex retinal images by skilled professionals. DR, a consequence of diabetes, has the potential to cause vision loss if not identified in its early stages. research presents a novel model that utilizes transfer learning to detect diabetic retinopathy. The first trials yielded poor accuracies for both training and validation. The categorization of class-4, especially those depicting severe instances, yielded unexpected outcomes, since several images were categorized as moderate. Consequently, more investigation is required to ascertain the accuracy of these classifications. Therefore, our suggested approach allows for easy classification of the retina pictures of both Diabetic and Healthy individuals, resulting in a decrease in the number of evaluations required by medical specialists.

关键词： Training Diabetic retinopathy Accuracy Transfer learning Refining Retina Data augmentation Robustness Research and development Medical diagnostic imaging

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

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Procedia computer Science 2025年 258卷 1576-1585页

作者： Tina Babu Rekha R Nair Kishore S Sheila Mahapatra Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte University (Deemed to be University) Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD.

关键词： Autism Spectrum Disorder (ASD) Machine Learning Bayesian Optimization Random Forest Early Diagnosis Predictive Modeling

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Balancing Fairness and Accuracy for Predictive Models in Criminal Justice Applications Using Multi-Objective Optimization Methods

Balancing Fairness and Accuracy for Predictive Models in Cri...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaowen Qi Yujungrong Ma Kiminori Nakamura Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering and Institute for Advanced Computer Studies University of Maryland College Park MD USA Department of Criminology and Criminal Justice University of Mary-land College Park MD USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments.

关键词： Accuracy Decision making Semantics Refining Optimization methods Evolutionary computation Predictive models Prediction algorithms Spatiotemporal phenomena Decision trees

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Efficient Hardware Implementation of Artificial Neural Networks on FPGA

Efficient Hardware Implementation of Artificial Neural Netwo...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Kasem Khalil Tamador Mohaidat Mahmoud Darwich Ashok Kumar Magdy Bayoumi Department of Electrical and Computer Engineering University of Mississippi Mississippi USA Department of Electrical Engineering Assiut University Egypt Department of Mathematics and Computer Science University of Mount Union Alliance OH USA The Center for Advanced Computer Studies University of Louisiana at Lafayette LA USA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA USA

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

A neural network finds widespread applications across various domains, with the primary challenge being the design of a network characterized by low area and power consumption. This paper introduces an efficient design strategy for a cost-effective neural network architecture. It leverages a shared multiplier and accumulator arrangement between neighboring nodes, halving the number of these units and subsequently reducing the overall area of the neural network. A configuration mechanism is employed to synchronize the operation between the two common nodes. The proposed method is implemented and tested using VHDL on an Altera 10 GX FPGA, and evaluations include testing with two datasets: MNIST and CIFAR-10. The results demonstrate that the proposed method maintains classification accuracy while achieving a 76% reduction in area compared to traditional methods, with a power consumption of 82 mW. These findings underscore the efficiency and suitability of the proposed method for applications prioritizing both area and energy efficiency.

关键词： Power demand VHDL Accuracy Circuits and systems Hardware Energy efficiency Synchronization

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